Selenium in the Prevention and Treatment of Hepatocellular Carcinoma: From Biomedical Investigation to Clinical Application

Selenium is a micronutrient that had been suggested to reduce the risk of cancer. Hepatocellular carcinoma (HCC), a prevalent disease and one of the most lethal cancers in the world, awaits new alternative treatment strategies to improve patients’ survival. As an essential trace element, selenium has been studied for its anticancer properties in both oxidative stress and inflammatory-related mechanisms that may contribute to HCC growth and metastasis. In recent decades, increasing studies have investigated the potential role of selenium in liver cancer involving several major cancer-associated signaling pathways, metabolic pathways, and antioxidant defense systems both in vitro and in preclinical models. It was also observed that there was an increase in the trend of development of novel selenium nanoparticles and selenium-containing inhibitors aiming to improve the therapeutic efficacy and relative potency of selenium. However, controversies remain with whether a relationship exists between serum selenium level and HCC risk. This chapter aims to summarize the multi-target and multi-pathway in vitro and in vivo pharmacological effects of selenium in HCC, to provide a more comprehensive view and to highlight the recently discovered molecular mechanisms We hope this chapter could outline the correlation of selenium level and the risk of HCC in patients and discuss the clinical application of selenium in HCC prevention and treatment

Interactive Causal Correlation Space Reshape for Multi-Label Classification

Most existing multi-label classification models focus on distance metrics and feature spare strategies to extract specific features of labels. Those models use the cosine similarity to construct the label correlation matrix to constraint solution space, and then mine the latent semantic information of the label space. However, the label correlation matrix is usually directly added to the model, which ignores the interactive causality of the correlation between the labels. Considering the label-specific features based on the distance method merely may have the problem of distance measurement failure in the high-dimensional space, while based on the sparse weight matrix method may cause the problem that parameter is dependent on manual selection. Eventually, this leads to poor classifier performance. In addition, it is considered that logical labels cannot describe the importance of different labels and cannot fully express semantic information. Based on these, we propose an Interactive Causal Correlation Space Reshape for Multi-Label Classification (CCSRMC) algorithm. Firstly, the algorithm constructs the label propagation matrix using characteristic that similar instances can be linearly represented by each other. Secondly, label co-occurrence matrix is constructed by combining the conditional probability test method, which is based on the label propagation reshaping the label space to rich label semantics. Then the label co-occurrence matrix combines with the label correlation matrix to construct the label interactive causal correlation matrix to perform multi-label classification learning on the obtained numerical label matrix. Finally, the algorithm in this paper is compared with multiple advanced algorithms on multiple benchmark multi-label datasets. The results show that considering the interactive causal label correlation can reduce the redundant information in the model and improve the performance of the multi-label classifier

Nowhere to Go: Benchmarking Multi-robot Collaboration in Target Trapping Environment

Collaboration is one of the most important factors in multi-robot systems. Considering certain real-world applications and to further promote its development, we propose a new benchmark to evaluate multi-robot collaboration in Target Trapping Environment (T2E). In T2E, two kinds of robots (called captor robot and target robot) share the same space. The captors aim to catch the target collaboratively, while the target will try to escape from the trap. Both the trapping and escaping process can use the environment layout to help achieve the corresponding objective, which requires high collaboration between robots and the utilization of the environment. For the benchmark, we present and evaluate multiple learning-based baselines in T2E, and provide insights into regimes of multi-robot collaboration. We also make our benchmark publicly available and encourage researchers from related robotics disciplines to propose, evaluate, and compare their solutions in this benchmark. Our project is released at https://github.com/Dr-Xiaogaren/T2E

Single-cell transcriptome profiling highlights the role of APP in blood vessels in assessing the risk of patients with proliferative diabetic retinopathy developing Alzheimer’s disease

Introduction: The incidence of diabetic retinopathy (DR) has been found to be associated with the risk of developing Alzheimer‘s disease (AD). In addition to the common properties of neurodegeneration, their progressions are involved with abnormal vascular functions. However, the interactions between them have not been fully understood. This study aimed to investigate the key factor for the underlying interactions and shared signaling pathways in the vasculature of DR and AD.Methods: We retrieved single-cell RNA sequencing (scRNA-seq) data regarding human fibrovascular membrane (FVM) of proliferative diabetic retinopathy (PDR) and human hippocampus vessels of AD from the NCBI-GEO database. GSEA analysis was performed to analyze AD-related genes in endothelial cells and pericytes of PDR. CellChat was used for predicting cell-cell communication and the signaling pathway.Results: The data suggested that amyloid-beta precursor protein (APP) signaling was found crucial in the vasculature of PDR and AD. Endothelial cells and pericytes could pose influences on other cells mainly via APP signaling in PDR. The endothelial cells were mainly coordinated with macrophages in the hippocampus vasculature of AD via APP signaling. The bulk RNA-seq in mice with PDR validated that the expression of APP gene had a significant correlation with that of the AD genome-wide association studies (GWAS) gene.Discussion: Our study demonstrates that the vasculopathy of PDR and AD is likely to share a common signaling pathway, of which the APP-related pathway is a potential target

Loss of endothelial hypoxia inducible factor-prolyl hydroxylase 2 induces cardiac hypertrophy and fibrosis

BACKGROUND: Cardiac hypertrophy and fibrosis are common adaptive responses to injury and stress, eventually leading to heart failure. Hypoxia signaling is important to the (patho)physiological process of cardiac remodeling. However, the role of endothelial PHD2 (prolyl-4 hydroxylase 2)/hypoxia inducible factor (HIF) signaling in the pathogenesis of cardiac hypertrophy and heart failure remains elusive. METHODS AND RESULTS: Mice with Egln1Tie2Cre (Tie2-Cre-mediated deletion of Egln1 [encoding PHD2]) exhibited left ventricular hypertrophy evident by increased thickness of anterior and posterior wall and left ventricular mass, as well as cardiac fibrosis. Tamoxifen-induced endothelial Egln1 deletion in adult mice also induced left ventricular hypertrophy and fibrosis. Additionally, we observed a marked decrease of PHD2 expression in heart tissues and cardiovascular endothelial cells from patients with cardiomyopathy. Moreover, genetic ablation of Hif2a but not Hif1a in Egln1Tie2Cre mice normalized cardiac size and function. RNA sequencing analysis also demonstrated HIF-2α as a critical mediator of signaling related to cardiac hypertrophy and fibrosis. Pharmacological inhibition of HIF-2α attenuated cardiac hypertrophy and fibrosis in Egln1Tie2Cre mice. CONCLUSIONS: The present study defines for the first time an unexpected role of endothelial PHD2 deficiency in inducing cardiac hypertrophy and fibrosis in an HIF-2α– dependent manner. PHD2 was markedly decreased in cardiovascular endothelial cells in patients with cardiomyopathy. Thus, targeting PHD2/HIF-2α signaling may represent a novel therapeutic approach for the treatment of pathological cardiac hypertrophy and failure

Longitudinal Association between Selenium Levels and Hypertension in a Rural Elderly Chinese Cohort

Objectives Results from previous studies have been inconsistent on the association between selenium and hypertension, and very few studies on this subject have focused on the elderly population. The purpose of this study is to examine the relationship between selenium level and hypertension in a rural elderly Chinese cohort. Design A longitudinal study was implemented and data were analyzed using logistic regression models and Cox proportional hazards regression model adjusting for potential confounders. The associations between selenium level and prevalent hypertension at baseline and between selenium and incident hypertension were examined. Setting Community-based setting in four rural areas in China. Subjects A total of 2000 elderly aged 65 years and over (mean 71.9±5.6 years) participated in this study. Measurements Nail selenium levels were measured in all subjects at baseline. Blood pressure measures and self-reported hypertension history were collected at baseline, 2.5 years and 7 years later. Hypertension was defined as systolic blood pressure 140 mmHg or higher, diastolic blood pressure 90 mmHg or higher, or reported use of anti-hypertensive medication. Results The rate of baseline hypertension was 63.50% in this cohort and the mean nail selenium level is 0.413±0.183µg/g. Multi-covariate adjusted cross-sectional analyses indicated that higher selenium level was associated with higher blood pressure measures at baseline and higher rates of hypertension. For the 635 participants with normal blood pressure at baseline, 360 had developed hypertension during follow-up. The incidence rate for hypertension was 45.83%, 52.27%, 62.50%, 70.48%, and 62.79% from the first selenium quintile to the fifth quintile respectively. Comparing to the lowest quintile group, the hazard ratios were 1.41 (95%CI: 1.03 to1.94), 1.93 (95%CI: 1.40 to 2.67), 2.35 (95%CI: 1.69 to 3.26) and 1.94 (95%CI: 1.36 to 22.77) for the second selenium quintile to the fifth quintile respectively. Conclusions Our findings suggest that high selenium may play a harmful role in the development of hypertension. Future studies are needed to confirm our findings and to elucidate a plausible biological mechanism